






'''simple docstring'''







import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__lowerCAmelCase        =							"\\n    Text data.\n    Second line of data."

__lowerCAmelCase        =							"file"


@pytest.fixture(scope='session'   )
def       __UpperCamelCase					(      lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			tmp_path_factory.mktemp('data'   ) / (FILE_PATH + '.zstd')
       a_						     =			bytes(lowercase_   ,       'utf-8'   )
       with zstd.open(lowercase_   ,       'wb'   ) as f:
              f.write(lowercase_   )
       return path


@pytest.fixture
def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       with open(os.path.join(tmpfs.local_root_dir   ,       lowercase_   )   ,       'w'   ) as f:
              f.write(lowercase_   )
       return FILE_PATH


@pytest.mark.parametrize('compression_format'   ,       ['gzip', 'xz', 'zstd']   )
def       __UpperCamelCase					(      lowercase_       :  List[str]   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  Optional[int]   ,       lowercase_       :  Union[str, Any]   ,       lowercase_       :  int   ,       lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			{'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
       a_						     =			input_paths[compression_format]
       a_						     =			tmp_path / 'cache'
       a_						     =			DownloadConfig(cache_dir=lowercase_   ,       extract_compressed_file=lowercase_   )
       a_						     =			cached_path(lowercase_   ,       download_config=lowercase_   )
       with open(lowercase_   ) as f:
              a_						     =			f.read()
       with open(lowercase_   ) as f:
              a_						     =			f.read()
       assert extracted_file_content == expected_file_content


@pytest.mark.parametrize('default_extracted'   ,       [True, False]   )
@pytest.mark.parametrize('default_cache_dir'   ,       [True, False]   )
def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  Dict   ,       lowercase_       :  int   ,       lowercase_       :  str   ,       lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			'custom_cache'
       a_						     =			'custom_extracted_dir'
       a_						     =			tmp_path / 'custom_extracted_path'
       if default_extracted:
              a_						     =			('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
       else:
              monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'   ,       lowercase_   )
              monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,       str(lowercase_   )   )
              a_						     =			custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

       a_						     =			xz_file
       a_						     =			(
           DownloadConfig(extract_compressed_file=lowercase_   )
           if default_cache_dir
           else DownloadConfig(cache_dir=tmp_path / custom_cache_dir   ,       extract_compressed_file=lowercase_   )
       )
       a_						     =			cached_path(lowercase_   ,       download_config=lowercase_   )
       assert Path(lowercase_   ).parent.parts[-2:] == expected


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			str(Path(lowercase_   ).resolve()   )
       assert cached_path(lowercase_   ) == text_file
       # relative path
       a_						     =			str(Path(lowercase_   ).resolve().relative_to(Path(os.getcwd()   )   )   )
       assert cached_path(lowercase_   ) == text_file


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       a_						     =			str(tmp_path.resolve() / '__missing_file__.txt'   )
       with pytest.raises(lowercase_   ):
              cached_path(lowercase_   )
       # relative path
       a_						     =			'./__missing_file__.txt'
       with pytest.raises(lowercase_   ):
              cached_path(lowercase_   )


def       __UpperCamelCase					(      lowercase_       :  Dict   ):
       """simple docstring"""




       a_						     =			get_from_cache(F'tmp://{tmpfs_file}'   )
       with open(lowercase_   ) as f:
              a_						     =			f.read()
       assert output_file_content == FILE_CONTENT


@patch('datasets.config.HF_DATASETS_OFFLINE'   ,       lowercase_   )
def       __UpperCamelCase					(      ):
       """simple docstring"""




       with pytest.raises(lowercase_   ):
              cached_path('https://huggingface.co'   )


@patch('datasets.config.HF_DATASETS_OFFLINE'   ,       lowercase_   )
def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ):
       """simple docstring"""




       a_						     =			tmp_path_factory.mktemp('data'   ) / 'file.html'
       with pytest.raises(lowercase_   ):
              http_get('https://huggingface.co'   ,       temp_file=lowercase_   )
       with pytest.raises(lowercase_   ):
              http_head('https://huggingface.co'   )


@patch('datasets.config.HF_DATASETS_OFFLINE'   ,       lowercase_   )
def       __UpperCamelCase					(      lowercase_       :  Dict   ):
       """simple docstring"""




       a_						     =			tmp_path_factory.mktemp('data'   ) / 'file.html'
       with pytest.raises(lowercase_   ):
              ftp_get('ftp://huggingface.co'   ,       temp_file=lowercase_   )
       with pytest.raises(lowercase_   ):
              ftp_head('ftp://huggingface.co'   )


@patch('datasets.config.HF_DATASETS_OFFLINE'   ,       lowercase_   )
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       a_						     =			tmp_path_factory.mktemp('data'   ) / 'file.html'
       with pytest.raises(lowercase_   ):
              fsspec_get('s3://huggingface.co'   ,       temp_file=lowercase_   )
       with pytest.raises(lowercase_   ):
              fsspec_head('s3://huggingface.co'   )












'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger("transformers.models.speecht5")


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  Dict   ,       lowercase_       :  Any   ):
       """simple docstring"""




       hf_model.apply_weight_norm()

       a_						     =			checkpoint['input_conv.weight_g']
       a_						     =			checkpoint['input_conv.weight_v']
       a_						     =			checkpoint['input_conv.bias']

       for i in range(len(config.upsample_rates   )   ):
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_g']
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_v']
              a_						     =			checkpoint[F'upsamples.{i}.1.bias']

       for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
              for j in range(len(config.resblock_dilation_sizes   )   ):
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

       a_						     =			checkpoint['output_conv.1.weight_g']
       a_						     =			checkpoint['output_conv.1.weight_v']
       a_						     =			checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  List[str]=None   ,       lowercase_       :  Tuple=None   ,       ):
       """simple docstring"""




       if config_path is not None:
              a_						     =			SpeechTaHifiGanConfig.from_pretrained(lowercase_   )
       else:
              a_						     =			SpeechTaHifiGanConfig()

       a_						     =			SpeechTaHifiGan(lowercase_   )

       a_						     =			torch.load(lowercase_   )
       load_weights(orig_checkpoint['model']['generator']   ,       lowercase_   ,       lowercase_   )

       a_						     =			np.load(lowercase_   )
       a_						     =			stats[0].reshape(-1   )
       a_						     =			stats[1].reshape(-1   )
       a_						     =			torch.from_numpy(lowercase_   ).float()
       a_						     =			torch.from_numpy(lowercase_   ).float()

       model.save_pretrained(lowercase_   )

       if repo_id:
              print('Pushing to the hub...'   )
              model.push_to_hub(lowercase_   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
  parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __lowerCAmelCase        =							parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )




